Prompt engineering cannot compensate for poor software architecture. When AI systems fail, it's usually because they're faithfully executing within broken abstractions—fragmented domains, inconsistent data models, and scattered business logic. LLMs amplify existing architectural problems rather than solving them. AI agents behave like distributed systems with state, latency, and failure modes that prompts can't fix. Good architecture makes AI predictable and boring; bad architecture makes it appear magical until production reality hits. The real value of AI may be exposing architectural debt that teams have been ignoring.
